This project aims to characterize the local changes in pancreatic islet inflammation (referred to as insulitis) and volume loss associated with type-1 diabetes (T1D) by developing novel computational image analysis algorithms that quantify such changes from magnetic resonance imaging (MRI) data. In the United States alone, as many as three million people may have T1D, with 80 new cases diagnosed every day, costing almost $15B annually (source: JDRF). Understanding the mechanisms of autoimmune destruction of cells at the organ level is important for developing new early diagnostic criteria and effective treatment strategies and preventative therapies. Clinical occultness of much of the autoimmune process, along with the difficult access to the location of the endocrine islets of Langerhans have slowed progress in understanding the etiology and progression of T1D. However, MRI alleviates this by permitting noninvasive, local measurement of pancreatic anatomy (such as the volume), in addition to insulitis via the use of magnetic nanoparticle (MNP) agents, making cross-sectional and longitudinal T1D imaging studies feasible. To that end, accurate correspondence among pancreatic regions of two or more images are required in order to compute 1) insulitis from pre/post- infusion MNP-MR images, 2) the progress of insulitis over time, and 3) the local change in pancreatic volume over time, in addition to 4) comparing all of these quantities across subjects. Such a point-wise correspondence is provided by image registration (alignment). This proposal aims to build on my background in brain image analysis and develop novel image registration (alignment) tools to accurately compute point- wise correspondence between pancreas images acquired from different subjects at different times, and subsequently use them in cross-sectional and longitudinal pancreatic imaging to develop new biomarkers, by locally tracking long-term inflammatory and volume changes in individuals with clinical and/or occult T1D. Specifically, we propose to develop an inherently-symmetric quasi-volume-preserving (QVP) non-rigid image registration algorithm for the pancreas, which, in contrast to the existing algorithms in the literature, avoids regional biases and the concomitant errors by defining a uniform objective function. Furthermore, the intergroup differences and intragroup variability are measured by constructing unbiased statistical pancreatic atlases of healthy and T1D cohorts, using a novel, improved group-wise registration algorithm. My long-term career goal is to establish and direct an inter-disciplinary research program at a top-notch academic institution, which will focus on developing creative approaches and innovative computational tools for processing biomedical images, in order to facilitate the investigation of the relationship between medical images and clinical data, and improve patient diagnosis and outcomes. My main objective for the K01 award period is to become an expert in T1D, in addition to abdominal - and especially pancreatic - MR acquisition and image analysis, and to advance this field by translating the skills I had previously acquired in brain image reconstruction and analysis. To achieve this goal, there are three important areas where I require additional training, mentoring, and experience: 1) diabetes, 2) abdominal imaging with contrast agents, and 3) advanced study design and biostatistics. I propose to acquire this training through direct mentoring, didactic coursework, modular courses, seminar series, and scientific meetings. The proposed project will form the foundation of my independent computational abdominal imaging research program, which will have diabetes at the core of its clinical focus.